ArticleScientific data2024
Harnessing Big Data in Critical Care: Exploring a new European Dataset.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Hourly Blood Urea Nitrogen Trajectories and ICU Mortality Risk in Critically Ill Patients with Gastrointestinal Bleeding: A Multicenter Retrospective Cohort Study.Advances in therapy · 2026Article
- Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.Journal of ovarian research · 2026Article
- Respiratory mechanics and patient-ventilator interaction dataset from the ICU.Scientific data · 2026Article
- Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness.NPJ digital medicine · 2026Article
- Multi-algorithm consensus classification identifies three distinct acute liver failure subtypes with differential treatment responses: a multi-database cohort study.Journal of advanced research · 2026Article
- The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- Rethinking Preoperative Risk Evaluation: How Well Does EuroSCORE II Predict Long-Term Mortality After Cardiac Surgery?-A Single-Centre Retrospective Analysis.Journal of clinical medicine · 2026Article
- Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis.Clinical kidney journal · 2026Article
- Development and validation of the Hypotensive Exposure Duration Index for mortality risk prediction in critically ill patients.Journal of intensive care · 2025Article
- Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Causal Machine Learning Approach.Critical care explorations · 2025Article
- The association between week-one red blood cell distribution width and one-year survival in critically ill patients: propensity score-based multicenter analysis.European journal of medical research · 2025Article
- ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.Critical care (London, England) · 2025Article
- Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Freely available datasets have become an invaluable tool to propel data-driven research, especially in the field of critical care medicine. However, the number of datasets available is limited. This leads to the repeated reuse of datasets, inherently increasing the risk of selection bias. Additionally, the need arose to validate insights derived from one dataset with another. In 2023, the Salzburg Intensive Care database (SICdb) was introduced. SICdb offers insights in currently 27,386 intensive care admissions from 21,583 patients. It contains cases of general and surgical intensive care from all disciplines. Amongst others SICdb contains information about: diagnosis, therapies (including data on preceding surgeries), scoring, laboratory values, respiratory and vital signals, and configuration data. Data for SICdb (1.0.6) was collected at one single tertiary care institution of the Department of Anesthesiology and Intensive Care Medicine at the Salzburger Landesklinik (SALK) and Paracelsus Medical University (PMU) between 2013 and 2021. This article aims to elucidate on the characteristics of the dataset, the technical implementation, and provides analysis of its strengths and limitations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.